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Merge pull request #40 from borglab/feature/factor_graph_update 

Factor graph update and add_factors method
release/4.3a0
Frank Dellaert 2019-06-02 02:03:29 -04:00 committed by GitHub
parent e439629349 e7f41694c1
commit 51fb1b6696
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GPG Key ID: 4AEE18F83AFDEB23
4 changed files with 556 additions and 489 deletions
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53
gtsam/inference/FactorGraph-inst.h
View File

@ -26,33 +26,34 @@
#include <boost/bind.hpp>
#include <stdio.h>
#include <sstream>
#include <algorithm>
#include <iostream> // for cout :-(
#include <sstream>
#include <string>
namespace gtsam {
/* ************************************************************************* */
template <class FACTOR>
void FactorGraph<FACTOR>::print(const std::string& s, const KeyFormatter& formatter) const {
void FactorGraph<FACTOR>::print(const std::string& s,
const KeyFormatter& formatter) const {
std::cout << s << std::endl;
std::cout << "size: " << size() << std::endl;
for (size_t i = 0; i < factors_.size(); i++) {
std::stringstream ss;
ss << "factor " << i << ": ";
if (factors_[i])
factors_[i]->print(ss.str(), formatter);
if (factors_[i]) factors_[i]->print(ss.str(), formatter);
}
}
/* ************************************************************************* */
template <class FACTOR>
bool FactorGraph<FACTOR>::equals(const This& fg, double tol) const {
/** check whether the two factor graphs have the same number of factors_ */
// check whether the two factor graphs have the same number of factors.
if (factors_.size() != fg.size()) return false;
/** check whether the factors_ are the same */
// check whether the factors are the same, in same order.
for (size_t i = 0; i < factors_.size(); i++) {
// TODO: Doesn't this force order of factor insertion?
sharedFactor f1 = factors_[i], f2 = fg.factors_[i];
if (f1 == NULL && f2 == NULL) continue;
if (f1 == NULL || f2 == NULL) return false;
@ -75,8 +76,7 @@ namespace gtsam {
KeySet FactorGraph<FACTOR>::keys() const {
KeySet keys;
for (const sharedFactor& factor : this->factors_) {
if(factor)
keys.insert(factor->begin(), factor->end());
if (factor) keys.insert(factor->begin(), factor->end());
}
return keys;
}
@ -87,8 +87,7 @@ namespace gtsam {
KeyVector keys;
keys.reserve(2 * size()); // guess at size
for (const sharedFactor& factor : factors_)
if (factor)
keys.insert(keys.end(), factor->begin(), factor->end());
if (factor) keys.insert(keys.end(), factor->begin(), factor->end());
std::sort(keys.begin(), keys.end());
auto last = std::unique(keys.begin(), keys.end());
keys.erase(last, keys.end());
@ -96,4 +95,36 @@ namespace gtsam {
}
/* ************************************************************************* */
template <class FACTOR>
template <typename CONTAINER, typename>
FactorIndices FactorGraph<FACTOR>::add_factors(const CONTAINER& factors,
bool useEmptySlots) {
const size_t num_factors = factors.size();
FactorIndices newFactorIndices(num_factors);
if (useEmptySlots) {
size_t i = 0;
for (size_t j = 0; j < num_factors; ++j) {
// Loop to find the next available factor slot
do {
if (i >= size())
// Make room for remaining factors, happens only once.
resize(size() + num_factors - j);
else if (at(i))
++i; // Move on to the next slot or past end.
else
break; // We found an empty slot, break to fill it.
} while (true);
// Use the current slot, updating graph and newFactorSlots.
at(i) = factors[j];
newFactorIndices[j] = i;
}
} else {
// We're not looking for unused slots, so just add the factors at the end.
for (size_t i = 0; i < num_factors; ++i) newFactorIndices[i] = i + size();
push_back(factors);
}
return newFactorIndices;
}
} // namespace gtsam

326
gtsam/inference/FactorGraph.h
View File

@ -22,77 +22,103 @@
#pragma once
#include <gtsam/base/Testable.h>
#include <gtsam/base/FastVector.h>
#include <gtsam/base/Testable.h>
#include <gtsam/inference/Key.h>
#include <Eigen/Core> // for Eigen::aligned_allocator
#include <boost/serialization/nvp.hpp>
#include <boost/serialization/vector.hpp>
#include <boost/assign/list_inserter.hpp>
#include <boost/bind.hpp>
#include <boost/make_shared.hpp>
#include <boost/serialization/nvp.hpp>
#include <boost/serialization/vector.hpp>
#include <string>
#include <type_traits>
#include <utility>
namespace gtsam {
/// Define collection type:
typedef FastVector<FactorIndex> FactorIndices;
// Forward declarations
template<class CLIQUE> class BayesTree;
template <class CLIQUE>
class BayesTree;
/** Helper */
template <class C>
class CRefCallPushBack
{
class CRefCallPushBack {
C& obj;
public:
CRefCallPushBack(C& obj) : obj(obj) {}
explicit CRefCallPushBack(C& obj) : obj(obj) {}
template <typename A>
void operator()(const A& a) { obj.push_back(a); }
void operator()(const A& a) {
obj.push_back(a);
}
};
/** Helper */
template <class C>
class RefCallPushBack
{
class RefCallPushBack {
C& obj;
public:
RefCallPushBack(C& obj) : obj(obj) {}
explicit RefCallPushBack(C& obj) : obj(obj) {}
template <typename A>
void operator()(A& a) { obj.push_back(a); }
void operator()(A& a) {
obj.push_back(a);
}
};
/** Helper */
template <class C>
class CRefCallAddCopy
{
class CRefCallAddCopy {
C& obj;
public:
CRefCallAddCopy(C& obj) : obj(obj) {}
explicit CRefCallAddCopy(C& obj) : obj(obj) {}
template <typename A>
void operator()(const A& a) { obj.addCopy(a); }
void operator()(const A& a) {
obj.addCopy(a);
}
};
/**
* A factor graph is a bipartite graph with factor nodes connected to variable nodes.
* In this class, however, only factor nodes are kept around.
* A factor graph is a bipartite graph with factor nodes connected to variable
* nodes. In this class, however, only factor nodes are kept around.
* \nosubgrouping
*/
template <class FACTOR>
class FactorGraph {
public:
typedef FACTOR FactorType; ///< factor type
typedef boost::shared_ptr<FACTOR> sharedFactor; ///< Shared pointer to a factor
typedef boost::shared_ptr<FACTOR>
sharedFactor; ///< Shared pointer to a factor
typedef sharedFactor value_type;
typedef typename FastVector<sharedFactor>::iterator iterator;
typedef typename FastVector<sharedFactor>::const_iterator const_iterator;
private:
typedef FactorGraph<FACTOR> This; ///< Typedef for this class
typedef boost::shared_ptr<This> shared_ptr; ///< Shared pointer for this class
typedef boost::shared_ptr<This>
shared_ptr; ///< Shared pointer for this class
/// Check if a DERIVEDFACTOR is in fact derived from FactorType.
template <typename DERIVEDFACTOR>
using IsDerived = typename std::enable_if<
std::is_base_of<FactorType, DERIVEDFACTOR>::value>::type;
/// Check if T has a value_type derived from FactorType.
template <typename T>
using HasDerivedValueType = typename std::enable_if<
std::is_base_of<FactorType, typename T::value_type>::value>::type;
/// Check if T has a value_type derived from FactorType.
template <typename T>
using HasDerivedElementType = typename std::enable_if<std::is_base_of<
FactorType, typename T::value_type::element_type>::value>::type;
protected:
/** concept check, makes sure FACTOR defines print and equals */
@ -109,40 +135,20 @@ namespace gtsam {
/** Constructor from iterator over factors (shared_ptr or plain objects) */
template <typename ITERATOR>
FactorGraph(ITERATOR firstFactor, ITERATOR lastFactor) { push_back(firstFactor, lastFactor); }
FactorGraph(ITERATOR firstFactor, ITERATOR lastFactor) {
push_back(firstFactor, lastFactor);
}
/** Construct from container of factors (shared_ptr or plain objects) */
template <class CONTAINER>
explicit FactorGraph(const CONTAINER& factors) { push_back(factors); }
/// @}
/// @name Advanced Constructors
/// @{
// TODO: are these needed?
///**
// * @brief Constructor from a Bayes net
// * @param bayesNet the Bayes net to convert, type CONDITIONAL must yield compatible factor
// * @return a factor graph with all the conditionals, as factors
// */
//template<class CONDITIONAL>
//FactorGraph(const BayesNet<CONDITIONAL>& bayesNet);
///** convert from Bayes tree */
//template<class CONDITIONAL, class CLIQUE>
//FactorGraph(const BayesTree<CONDITIONAL, CLIQUE>& bayesTree);
///** convert from a derived type */
//template<class DERIVEDFACTOR>
//FactorGraph(const FactorGraph<DERIVEDFACTOR>& factors) {
// factors_.assign(factors.begin(), factors.end());
//}
explicit FactorGraph(const CONTAINER& factors) {
push_back(factors);
}
/// @}
public:
/// @name Adding Factors
/// @name Adding Single Factors
/// @{
/**
@ -151,110 +157,130 @@ namespace gtsam {
*/
void reserve(size_t size) { factors_.reserve(size); }
// TODO: are these needed?
/** Add a factor directly using a shared_ptr */
/// Add a factor directly using a shared_ptr.
template <class DERIVEDFACTOR>
typename std::enable_if<std::is_base_of<FactorType, DERIVEDFACTOR>::value>::type
push_back(boost::shared_ptr<DERIVEDFACTOR> factor) {
factors_.push_back(boost::shared_ptr<FACTOR>(factor)); }
IsDerived<DERIVEDFACTOR> push_back(boost::shared_ptr<DERIVEDFACTOR> factor) {
factors_.push_back(boost::shared_ptr<FACTOR>(factor));
}
/** Add a factor directly using a shared_ptr */
void push_back(const sharedFactor& factor) {
factors_.push_back(factor); }
/** Emplace a factor */
/// Emplace a shared pointer to factor of given type.
template <class DERIVEDFACTOR, class... Args>
typename std::enable_if<std::is_base_of<FactorType, DERIVEDFACTOR>::value>::type
emplace_shared(Args&&... args) {
factors_.push_back(boost::allocate_shared<DERIVEDFACTOR>(Eigen::aligned_allocator<DERIVEDFACTOR>(), std::forward<Args>(args)...));
IsDerived<DERIVEDFACTOR> emplace_shared(Args&&... args) {
factors_.push_back(boost::allocate_shared<DERIVEDFACTOR>(
Eigen::aligned_allocator<DERIVEDFACTOR>(),
std::forward<Args>(args)...));
}
/** push back many factors with an iterator over shared_ptr (factors are not copied) */
template<typename ITERATOR>
typename std::enable_if<std::is_base_of<FactorType, typename ITERATOR::value_type::element_type>::value>::type
push_back(ITERATOR firstFactor, ITERATOR lastFactor) {
factors_.insert(end(), firstFactor, lastFactor); }
/** push back many factors as shared_ptr's in a container (factors are not copied) */
template<typename CONTAINER>
typename std::enable_if<std::is_base_of<FactorType, typename CONTAINER::value_type::element_type>::value>::type
push_back(const CONTAINER& container) {
push_back(container.begin(), container.end());
}
/** push back a BayesTree as a collection of factors. NOTE: This should be hidden in derived
* classes in favor of a type-specialized version that calls this templated function. */
template<class CLIQUE>
typename std::enable_if<std::is_base_of<This, typename CLIQUE::FactorGraphType>::value>::type
push_back(const BayesTree<CLIQUE>& bayesTree) {
bayesTree.addFactorsToGraph(*this);
}
//#ifdef GTSAM_ALLOW_DEPRECATED_SINCE_V4
/** Add a factor by value, will be copy-constructed (use push_back with a shared_ptr to avoid
* the copy). */
/**
* Add a factor by value, will be copy-constructed (use push_back with a
* shared_ptr to avoid the copy).
*/
template <class DERIVEDFACTOR>
typename std::enable_if<std::is_base_of<FactorType, DERIVEDFACTOR>::value>::type
push_back(const DERIVEDFACTOR& factor) {
factors_.push_back(boost::allocate_shared<DERIVEDFACTOR>(Eigen::aligned_allocator<DERIVEDFACTOR>(), factor));
}
//#endif
/** push back many factors with an iterator over plain factors (factors are copied) */
template<typename ITERATOR>
typename std::enable_if<std::is_base_of<FactorType, typename ITERATOR::value_type>::value>::type
push_back(ITERATOR firstFactor, ITERATOR lastFactor) {
for (ITERATOR f = firstFactor; f != lastFactor; ++f)
push_back(*f);
IsDerived<DERIVEDFACTOR> push_back(const DERIVEDFACTOR& factor) {
factors_.push_back(boost::allocate_shared<DERIVEDFACTOR>(
Eigen::aligned_allocator<DERIVEDFACTOR>(), factor));
}
/** push back many factors as non-pointer objects in a container (factors are copied) */
template<typename CONTAINER>
typename std::enable_if<std::is_base_of<FactorType, typename CONTAINER::value_type>::value>::type
push_back(const CONTAINER& container) {
push_back(container.begin(), container.end());
}
/** Add a factor directly using a shared_ptr */
/// `add` is a synonym for push_back.
template <class DERIVEDFACTOR>
typename std::enable_if<std::is_base_of<FactorType, DERIVEDFACTOR>::value,
IsDerived<DERIVEDFACTOR> add(boost::shared_ptr<DERIVEDFACTOR> factor) {
push_back(factor);
}
/// `+=` works well with boost::assign list inserter.
template <class DERIVEDFACTOR>
typename std::enable_if<
std::is_base_of<FactorType, DERIVEDFACTOR>::value,
boost::assign::list_inserter<RefCallPushBack<This>>>::type
operator+=(boost::shared_ptr<DERIVEDFACTOR> factor) {
return boost::assign::make_list_inserter(RefCallPushBack<This>(*this))(factor);
return boost::assign::make_list_inserter(RefCallPushBack<This>(*this))(
factor);
}
/** Add a factor directly using a shared_ptr */
boost::assign::list_inserter<CRefCallPushBack<This> >
operator+=(const sharedFactor& factor) {
return boost::assign::make_list_inserter(CRefCallPushBack<This>(*this))(factor);
/// @}
/// @name Adding via iterators
/// @{
/**
* Push back many factors with an iterator over shared_ptr (factors are not
* copied)
*/
template <typename ITERATOR>
HasDerivedElementType<ITERATOR> push_back(ITERATOR firstFactor,
ITERATOR lastFactor) {
factors_.insert(end(), firstFactor, lastFactor);
}
/** Add a factor or container of factors, including STL collections, BayesTrees, etc. */
template<class FACTOR_OR_CONTAINER>
boost::assign::list_inserter<CRefCallPushBack<This> >
operator+=(const FACTOR_OR_CONTAINER& factorOrContainer) {
return boost::assign::make_list_inserter(CRefCallPushBack<This>(*this))(factorOrContainer);
/// Push back many factors with an iterator (factors are copied)
template <typename ITERATOR>
HasDerivedValueType<ITERATOR> push_back(ITERATOR firstFactor,
ITERATOR lastFactor) {
for (ITERATOR f = firstFactor; f != lastFactor; ++f) push_back(*f);
}
/** Add a factor directly using a shared_ptr */
template<class DERIVEDFACTOR>
typename std::enable_if<std::is_base_of<FactorType, DERIVEDFACTOR>::value>::type
add(boost::shared_ptr<DERIVEDFACTOR> factor) {
push_back(factor);
/// @}
/// @name Adding via container
/// @{
/**
* Push back many factors as shared_ptr's in a container (factors are not
* copied)
*/
template <typename CONTAINER>
HasDerivedElementType<CONTAINER> push_back(const CONTAINER& container) {
push_back(container.begin(), container.end());
}
/** Add a factor directly using a shared_ptr */
void add(const sharedFactor& factor) {
push_back(factor);
/// Push back non-pointer objects in a container (factors are copied).
template <typename CONTAINER>
HasDerivedValueType<CONTAINER> push_back(const CONTAINER& container) {
push_back(container.begin(), container.end());
}
/** Add a factor or container of factors, including STL collections, BayesTrees, etc. */
/**
* Add a factor or container of factors, including STL collections,
* BayesTrees, etc.
*/
template <class FACTOR_OR_CONTAINER>
void add(const FACTOR_OR_CONTAINER& factorOrContainer) {
push_back(factorOrContainer);
}
/**
* Add a factor or container of factors, including STL collections,
* BayesTrees, etc.
*/
template <class FACTOR_OR_CONTAINER>
boost::assign::list_inserter<CRefCallPushBack<This>> operator+=(
const FACTOR_OR_CONTAINER& factorOrContainer) {
return boost::assign::make_list_inserter(CRefCallPushBack<This>(*this))(
factorOrContainer);
}
/// @}
/// @name Specialized versions
/// @{
/**
* Push back a BayesTree as a collection of factors.
* NOTE: This should be hidden in derived classes in favor of a
* type-specialized version that calls this templated function.
*/
template <class CLIQUE>
typename std::enable_if<
std::is_base_of<This, typename CLIQUE::FactorGraphType>::value>::type
push_back(const BayesTree<CLIQUE>& bayesTree) {
bayesTree.addFactorsToGraph(*this);
}
/**
* Add new factors to a factor graph and returns a list of new factor indices,
* optionally finding and reusing empty factor slots.
*/
template <typename CONTAINER, typename = HasDerivedElementType<CONTAINER>>
FactorIndices add_factors(const CONTAINER& factors,
bool useEmptySlots = false);
/// @}
/// @name Testable
/// @{
@ -271,29 +297,31 @@ namespace gtsam {
/// @name Standard Interface
/// @{
/** return the number of factors (including any null factors set by remove() ). */
/** return the number of factors (including any null factors set by remove()
* ). */
size_t size() const { return factors_.size(); }
/** Check if the graph is empty (null factors set by remove() will cause this to return false). */
/** Check if the graph is empty (null factors set by remove() will cause
* this to return false). */
bool empty() const { return factors_.empty(); }
/** Get a specific factor by index (this checks array bounds and may throw an exception, as
* opposed to operator[] which does not).
/** Get a specific factor by index (this checks array bounds and may throw
* an exception, as opposed to operator[] which does not).
*/
const sharedFactor at(size_t i) const { return factors_.at(i); }
/** Get a specific factor by index (this checks array bounds and may throw an exception, as
* opposed to operator[] which does not).
/** Get a specific factor by index (this checks array bounds and may throw
* an exception, as opposed to operator[] which does not).
*/
sharedFactor& at(size_t i) { return factors_.at(i); }
/** Get a specific factor by index (this does not check array bounds, as opposed to at() which
* does).
/** Get a specific factor by index (this does not check array bounds, as
* opposed to at() which does).
*/
const sharedFactor operator[](size_t i) const { return at(i); }
/** Get a specific factor by index (this does not check array bounds, as opposed to at() which
* does).
/** Get a specific factor by index (this does not check array bounds, as
* opposed to at() which does).
*/
sharedFactor& operator[](size_t i) { return at(i); }
@ -319,13 +347,14 @@ namespace gtsam {
/** non-const STL-style end() */
iterator end() { return factors_.end(); }
/** Directly resize the number of factors in the graph. If the new size is less than the
* original, factors at the end will be removed. If the new size is larger than the original,
* null factors will be appended.
/** Directly resize the number of factors in the graph. If the new size is
* less than the original, factors at the end will be removed. If the new
* size is larger than the original, null factors will be appended.
*/
void resize(size_t size) { factors_.resize(size); }
/** delete factor without re-arranging indexes by inserting a NULL pointer */
/** delete factor without re-arranging indexes by inserting a NULL pointer
*/
void remove(size_t i) { factors_[i].reset(); }
/** replace a factor by index */
@ -335,7 +364,9 @@ namespace gtsam {
iterator erase(iterator item) { return factors_.erase(item); }
/** Erase factors and rearrange other factors to take up the empty space */
iterator erase(iterator first, iterator last) { return factors_.erase(first, last); }
iterator erase(iterator first, iterator last) {
return factors_.erase(first, last);
}
/// @}
/// @name Advanced Interface
@ -344,17 +375,20 @@ namespace gtsam {
/** return the number of non-null factors */
size_t nrFactors() const;
/** Potentially slow function to return all keys involved, sorted, as a set */
/** Potentially slow function to return all keys involved, sorted, as a set
*/
KeySet keys() const;
/** Potentially slow function to return all keys involved, sorted, as a vector */
/** Potentially slow function to return all keys involved, sorted, as a
* vector
*/
KeyVector keyVector() const;
/** MATLAB interface utility: Checks whether a factor index idx exists in the graph and is a live pointer */
/** MATLAB interface utility: Checks whether a factor index idx exists in
* the graph and is a live pointer */
inline bool exists(size_t idx) const { return idx < size() && at(idx); }
private:
/** Serialization function */
friend class boost::serialization::access;
template <class ARCHIVE>
@ -363,9 +397,7 @@ namespace gtsam {
}
/// @}
}; // FactorGraph
} // namespace gtsam
#include <gtsam/inference/FactorGraph-inst.h>

11
gtsam/linear/GaussianBayesTree.cpp
View File

@ -35,10 +35,13 @@ namespace gtsam {
namespace internal
{
/* ************************************************************************* */
double logDeterminant(const GaussianBayesTreeClique::shared_ptr& clique, double& parentSum)
{
parentSum += clique->conditional()->R().diagonal().unaryExpr(std::ptr_fun<double,double>(log)).sum();
assert(false);
double logDeterminant(const GaussianBayesTreeClique::shared_ptr& clique,
double& parentSum) {
parentSum += clique->conditional()
->R()
.diagonal()
.unaryExpr(std::ptr_fun<double, double>(log))
.sum();
return 0;
}
}

255
gtsam/symbolic/tests/testSymbolicFactorGraph.cpp
View File

@ -15,14 +15,16 @@
* @author Christian Potthast
**/
#include <CppUnitLite/TestHarness.h>
#include <gtsam/symbolic/SymbolicFactorGraph.h>
#include <gtsam/base/TestableAssertions.h>
#include <gtsam/symbolic/SymbolicBayesNet.h>
#include <gtsam/symbolic/SymbolicBayesTree.h>
#include <gtsam/symbolic/SymbolicConditional.h>
#include <gtsam/symbolic/tests/symbolicExampleGraphs.h>
#include <CppUnitLite/TestHarness.h>
#include <boost/assign/std/set.hpp>
using namespace std;
@ -46,8 +48,7 @@ TEST(SymbolicFactorGraph, keys2) {
}
/* ************************************************************************* */
TEST(SymbolicFactorGraph, eliminateFullSequential)
{
TEST(SymbolicFactorGraph, eliminateFullSequential) {
// Test with simpleTestGraph1
Ordering order;
order += 0, 1, 2, 3, 4;
@ -60,19 +61,15 @@ TEST(SymbolicFactorGraph, eliminateFullSequential)
}
/* ************************************************************************* */
TEST(SymbolicFactorGraph, eliminatePartialSequential)
{
TEST(SymbolicFactorGraph, eliminatePartialSequential) {
// Eliminate 0 and 1
const Ordering order = list_of(0)(1);
const SymbolicBayesNet expectedBayesNet = list_of
(SymbolicConditional(0,1,2))
(SymbolicConditional(1,2,3,4));
const SymbolicBayesNet expectedBayesNet =
list_of(SymbolicConditional(0, 1, 2))(SymbolicConditional(1, 2, 3, 4));
const SymbolicFactorGraph expectedSfg = list_of
(SymbolicFactor(2,3))
(SymbolicFactor(4,5))
(SymbolicFactor(2,3,4));
const SymbolicFactorGraph expectedSfg = list_of(SymbolicFactor(2, 3))(
SymbolicFactor(4, 5))(SymbolicFactor(2, 3, 4));
SymbolicBayesNet::shared_ptr actualBayesNet;
SymbolicFactorGraph::shared_ptr actualSfg;
@ -85,39 +82,36 @@ TEST(SymbolicFactorGraph, eliminatePartialSequential)
SymbolicBayesNet::shared_ptr actualBayesNet2;
SymbolicFactorGraph::shared_ptr actualSfg2;
boost::tie(actualBayesNet2, actualSfg2) =
simpleTestGraph2.eliminatePartialSequential(list_of(0)(1).convert_to_container<KeyVector >());
simpleTestGraph2.eliminatePartialSequential(
list_of(0)(1).convert_to_container<KeyVector>());
EXPECT(assert_equal(expectedSfg, *actualSfg2));
EXPECT(assert_equal(expectedBayesNet, *actualBayesNet2));
}
/* ************************************************************************* */
TEST(SymbolicFactorGraph, eliminateFullMultifrontal)
{
Ordering ordering; ordering += 0,1,2,3;
SymbolicBayesTree actual1 =
*simpleChain.eliminateMultifrontal(ordering);
TEST(SymbolicFactorGraph, eliminateFullMultifrontal) {
Ordering ordering;
ordering += 0, 1, 2, 3;
SymbolicBayesTree actual1 = *simpleChain.eliminateMultifrontal(ordering);
EXPECT(assert_equal(simpleChainBayesTree, actual1));
SymbolicBayesTree actual2 =
*asiaGraph.eliminateMultifrontal(asiaOrdering);
SymbolicBayesTree actual2 = *asiaGraph.eliminateMultifrontal(asiaOrdering);
EXPECT(assert_equal(asiaBayesTree, actual2));
}
/* ************************************************************************* */
TEST(SymbolicFactorGraph, eliminatePartialMultifrontal)
{
TEST(SymbolicFactorGraph, eliminatePartialMultifrontal) {
SymbolicBayesTree expectedBayesTree;
SymbolicConditional::shared_ptr root = boost::make_shared<SymbolicConditional>(
SymbolicConditional::shared_ptr root =
boost::make_shared<SymbolicConditional>(
SymbolicConditional::FromKeys(list_of(4)(5)(1), 2));
expectedBayesTree.insertRoot(boost::make_shared<SymbolicBayesTreeClique>(root));
expectedBayesTree.insertRoot(
boost::make_shared<SymbolicBayesTreeClique>(root));
SymbolicFactorGraph expectedFactorGraph = list_of
(SymbolicFactor(0,1))
(SymbolicFactor(0,2))
(SymbolicFactor(1,3))
(SymbolicFactor(2,3))
(SymbolicFactor(1));
SymbolicFactorGraph expectedFactorGraph =
list_of(SymbolicFactor(0, 1))(SymbolicFactor(0, 2))(SymbolicFactor(1, 3))(
SymbolicFactor(2, 3))(SymbolicFactor(1));
SymbolicBayesTree::shared_ptr actualBayesTree;
SymbolicFactorGraph::shared_ptr actualFactorGraph;
@ -128,7 +122,8 @@ TEST(SymbolicFactorGraph, eliminatePartialMultifrontal)
EXPECT(assert_equal(expectedBayesTree, *actualBayesTree));
SymbolicBayesTree expectedBayesTree2;
SymbolicBayesTreeClique::shared_ptr root2 = boost::make_shared<SymbolicBayesTreeClique>(
SymbolicBayesTreeClique::shared_ptr root2 =
boost::make_shared<SymbolicBayesTreeClique>(
boost::make_shared<SymbolicConditional>(4, 1));
root2->children.push_back(boost::make_shared<SymbolicBayesTreeClique>(
boost::make_shared<SymbolicConditional>(5, 4)));
@ -137,20 +132,18 @@ TEST(SymbolicFactorGraph, eliminatePartialMultifrontal)
SymbolicBayesTree::shared_ptr actualBayesTree2;
SymbolicFactorGraph::shared_ptr actualFactorGraph2;
boost::tie(actualBayesTree2, actualFactorGraph2) =
simpleTestGraph2.eliminatePartialMultifrontal(list_of<Key>(4)(5).convert_to_container<KeyVector >());
simpleTestGraph2.eliminatePartialMultifrontal(
list_of<Key>(4)(5).convert_to_container<KeyVector>());
EXPECT(assert_equal(expectedFactorGraph, *actualFactorGraph2));
EXPECT(assert_equal(expectedBayesTree2, *actualBayesTree2));
}
/* ************************************************************************* */
TEST(SymbolicFactorGraph, marginalMultifrontalBayesNet)
{
SymbolicBayesNet expectedBayesNet = list_of
(SymbolicConditional(0, 1, 2))
(SymbolicConditional(1, 2, 3))
(SymbolicConditional(2, 3))
(SymbolicConditional(3));
TEST(SymbolicFactorGraph, marginalMultifrontalBayesNet) {
SymbolicBayesNet expectedBayesNet =
list_of(SymbolicConditional(0, 1, 2))(SymbolicConditional(1, 2, 3))(
SymbolicConditional(2, 3))(SymbolicConditional(3));
SymbolicBayesNet actual1 = *simpleTestGraph2.marginalMultifrontalBayesNet(
Ordering(list_of(0)(1)(2)(3)));
@ -181,90 +174,61 @@ TEST(SymbolicFactorGraph, eliminate_disconnected_graph) {
}
/* ************************************************************************* */
//TEST(SymbolicFactorGraph, marginals)
//{
// // Create factor graph
// SymbolicFactorGraph fg;
// fg.push_factor(0, 1);
// fg.push_factor(0, 2);
// fg.push_factor(1, 4);
// fg.push_factor(2, 4);
// fg.push_factor(3, 4);
//
// // eliminate
// SymbolicSequentialSolver solver(fg);
// SymbolicBayesNet::shared_ptr actual = solver.eliminate();
// SymbolicBayesNet expected;
// expected.push_front(boost::make_shared<IndexConditional>(4));
// expected.push_front(boost::make_shared<IndexConditional>(3, 4));
// expected.push_front(boost::make_shared<IndexConditional>(2, 4));
// expected.push_front(boost::make_shared<IndexConditional>(1, 2, 4));
// expected.push_front(boost::make_shared<IndexConditional>(0, 1, 2));
// EXPECT(assert_equal(expected,*actual));
//
// {
// // jointBayesNet
// vector<Index> js;
// js.push_back(0);
// js.push_back(4);
// js.push_back(3);
// SymbolicBayesNet::shared_ptr actualBN = solver.jointBayesNet(js);
// SymbolicBayesNet expectedBN;
// expectedBN.push_front(boost::make_shared<IndexConditional>(3));
// expectedBN.push_front(boost::make_shared<IndexConditional>(4, 3));
// expectedBN.push_front(boost::make_shared<IndexConditional>(0, 4));
// EXPECT( assert_equal(expectedBN,*actualBN));
//
// // jointFactorGraph
// SymbolicFactorGraph::shared_ptr actualFG = solver.jointFactorGraph(js);
// SymbolicFactorGraph expectedFG;
// expectedFG.push_factor(0, 4);
// expectedFG.push_factor(4, 3);
// expectedFG.push_factor(3);
// EXPECT( assert_equal(expectedFG,(SymbolicFactorGraph)(*actualFG)));
// }
//
// {
// // jointBayesNet
// vector<Index> js;
// js.push_back(0);
// js.push_back(2);
// js.push_back(3);
// SymbolicBayesNet::shared_ptr actualBN = solver.jointBayesNet(js);
// SymbolicBayesNet expectedBN;
// expectedBN.push_front(boost::make_shared<IndexConditional>(2));
// expectedBN.push_front(boost::make_shared<IndexConditional>(3, 2));
// expectedBN.push_front(boost::make_shared<IndexConditional>(0, 3, 2));
// EXPECT( assert_equal(expectedBN,*actualBN));
//
// // jointFactorGraph
// SymbolicFactorGraph::shared_ptr actualFG = solver.jointFactorGraph(js);
// SymbolicFactorGraph expectedFG;
// expectedFG.push_factor(0, 3, 2);
// expectedFG.push_factor(3, 2);
// expectedFG.push_factor(2);
// EXPECT( assert_equal(expectedFG,(SymbolicFactorGraph)(*actualFG)));
// }
//
// {
// // conditionalBayesNet
// vector<Index> js;
// js.push_back(0);
// js.push_back(2);
// js.push_back(3);
// size_t nrFrontals = 2;
// SymbolicBayesNet::shared_ptr actualBN = //
// solver.conditionalBayesNet(js, nrFrontals);
// SymbolicBayesNet expectedBN;
// expectedBN.push_front(boost::make_shared<IndexConditional>(2, 3));
// expectedBN.push_front(boost::make_shared<IndexConditional>(0, 2, 3));
// EXPECT( assert_equal(expectedBN,*actualBN));
// }
//}
TEST(SymbolicFactorGraph, marginals) {
// Create factor graph
SymbolicFactorGraph fg;
fg.push_factor(0, 1);
fg.push_factor(0, 2);
fg.push_factor(1, 4);
fg.push_factor(2, 4);
fg.push_factor(3, 4);
// eliminate
Ordering ord(list_of(3)(4)(2)(1)(0));
auto actual = fg.eliminateSequential(ord);
SymbolicBayesNet expected;
expected.emplace_shared<SymbolicConditional>(3, 4);
expected.emplace_shared<SymbolicConditional>(4, 1, 2);
expected.emplace_shared<SymbolicConditional>(2, 0, 1);
expected.emplace_shared<SymbolicConditional>(1, 0);
expected.emplace_shared<SymbolicConditional>(0);
EXPECT(assert_equal(expected, *actual));
{
// jointBayesNet
Ordering ord(list_of(0)(4)(3));
auto actual = fg.eliminatePartialSequential(ord);
SymbolicBayesNet expectedBN;
expectedBN.emplace_shared<SymbolicConditional>(0, 1, 2);
expectedBN.emplace_shared<SymbolicConditional>(4, 1, 2, 3);
expectedBN.emplace_shared<SymbolicConditional>(3, 1, 2);
EXPECT(assert_equal(expectedBN, *(actual.first)));
}
{
// jointBayesNet
Ordering ord(list_of(0)(2)(3));
auto actual = fg.eliminatePartialSequential(ord);
SymbolicBayesNet expectedBN;
expectedBN.emplace_shared<SymbolicConditional>(0, 1, 2);
expectedBN.emplace_shared<SymbolicConditional>(2, 1, 4);
expectedBN.emplace_shared<SymbolicConditional>(3, 4);
EXPECT(assert_equal(expectedBN, *(actual.first)));
}
{
// conditionalBayesNet
Ordering ord(list_of(0)(2));
auto actual = fg.eliminatePartialSequential(ord);
SymbolicBayesNet expectedBN;
expectedBN.emplace_shared<SymbolicConditional>(0, 1, 2);
expectedBN.emplace_shared<SymbolicConditional>(2, 1, 4);
EXPECT(assert_equal(expectedBN, *(actual.first)));
}
}
/* ************************************************************************* */
TEST( SymbolicFactorGraph, constructFromBayesNet )
{
TEST(SymbolicFactorGraph, constructFromBayesNet) {
// create expected factor graph
SymbolicFactorGraph expected;
expected.push_factor(0, 1, 2);
@ -284,8 +248,7 @@ TEST( SymbolicFactorGraph, constructFromBayesNet )
}
/* ************************************************************************* */
TEST( SymbolicFactorGraph, constructFromBayesTree )
{
TEST(SymbolicFactorGraph, constructFromBayesTree) {
// create expected factor graph
SymbolicFactorGraph expected;
expected.push_factor(_E_, _L_, _B_);
@ -300,8 +263,7 @@ TEST( SymbolicFactorGraph, constructFromBayesTree )
}
/* ************************************************************************* */
TEST( SymbolicFactorGraph, push_back )
{
TEST(SymbolicFactorGraph, push_back) {
// Create two factor graphs and expected combined graph
SymbolicFactorGraph fg1, fg2, expected;
@ -321,8 +283,47 @@ TEST( SymbolicFactorGraph, push_back )
actual.push_back(fg1);
actual.push_back(fg2);
CHECK(assert_equal(expected, actual));
// combine in second way
SymbolicFactorGraph actual2 = fg1;
actual2.push_back(fg2);
CHECK(assert_equal(expected, actual2));
}
/* ************************************************************************* */
int main() { TestResult tr; return TestRegistry::runAllTests(tr); }
TEST(SymbolicFactorGraph, add_factors) {
SymbolicFactorGraph fg1;
fg1.push_factor(10);
fg1 += SymbolicFactor::shared_ptr(); // empty slot!
fg1.push_factor(11);
SymbolicFactorGraph fg2;
fg2.push_factor(1);
fg2.push_factor(2);
SymbolicFactorGraph expected;
expected.push_factor(10);
expected.push_factor(1);
expected.push_factor(11);
expected.push_factor(2);
const FactorIndices expectedIndices = list_of(1)(3);
const FactorIndices actualIndices = fg1.add_factors(fg2, true);
EXPECT(assert_equal(expected, fg1));
EXPECT(assert_container_equality(expectedIndices, actualIndices));
expected.push_factor(1);
expected.push_factor(2);
const FactorIndices expectedIndices2 = list_of(4)(5);
const FactorIndices actualIndices2 = fg1.add_factors(fg2, false);
EXPECT(assert_equal(expected, fg1));
EXPECT(assert_container_equality(expectedIndices2, actualIndices2));
}
/* ************************************************************************* */
int main() {
TestResult tr;
return TestRegistry::runAllTests(tr);
}
/* ************************************************************************* */